Update backend.py

backend.py

@@ -1,371 +1,374 @@
-"""
-Meeting Minutes Generator - Backend API
-Handles audio transcription and minutes generation using Groq
-"""
-
-# ============================================
-# IMPORTS
-# ============================================
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from pydantic import BaseModel
-from typing import Optional
-from groq import Groq
-import os
-from dotenv import load_dotenv
-
-# ============================================
-# LOAD ENVIRONMENT VARIABLES
-# ============================================
-load_dotenv()  # Reads .env file and loads variables
-
-# ============================================
-# INITIALIZE FASTAPI APP
-# ============================================
-app = FastAPI(
-    title="Meeting Minutes API",
-    version="2.0.0",
-    description="Transcribe meeting audio and generate formatted minutes using Groq"
-)
-
-# ============================================
-# INITIALIZE GROQ CLIENT
-# ============================================
-# Get API key from environment
-GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
-
-# Validate API key exists
-if not GROQ_API_KEY:
-    raise ValueError("❌ GROQ_API_KEY not found in environment. Check your .env file!")
-
-# Create Groq client
-groq_client = Groq(api_key=GROQ_API_KEY)
-
-# ============================================
-# PYDANTIC MODELS (Type Safety & Documentation)
-# ============================================
-
-class TranscribeResponse(BaseModel):
-    """
-    Response model for successful transcription
-    """
-    transcript: str          # The transcribed text
-    file_size_mb: float      # Size of uploaded file in MB
-    filename: str            # Original filename
-    success: bool            # Always True for successful responses
-
-class GenerateMinutesRequest(BaseModel):
-    """
-    Request model for generating minutes
-    """
-    transcript: str          # Raw transcript text from /transcribe
-
-class GenerateMinutesResponse(BaseModel):
-    """
-    Response model for generated minutes
-    """
-    minutes: str            # Formatted Markdown minutes
-    success: bool           # Always True for successful responses
-
-class ErrorResponse(BaseModel):
-    """
-    Response model for errors
-    """
-    error: str              # Error message
-    detail: Optional[str]   # Additional error details
-
-# ============================================
-# CONSTANTS
-# ============================================
-
-# Transcription settings
-MAX_FILE_SIZE_MB = 25              # Groq Whisper limit
-WHISPER_MODEL = "whisper-large-v3"  # Most accurate Whisper model
-WHISPER_TEMPERATURE = 0.1           # Slight randomness for better transcription
-
-# Minutes generation settings
-LLM_MODEL = "openai/gpt-oss-120b"   # Groq LLM model for minutes
-LLM_TEMPERATURE = 0.1               # Low temperature for consistent, factual output
-MAX_COMPLETION_TOKENS = 1024        # Enough for any realistic meeting minutes
-
-# System prompt for minutes generation
-MINUTES_SYSTEM_PROMPT = """You are an assistant that converts meeting transcripts into concise, factual minutes.
-
-Your task:
-1. Remove filler words and disfluencies (uh, um, like, you know).
-2. Restore punctuation and sentence boundaries.
-3. Extract clear, factual minutes.
-4. Do NOT invent facts. If unclear, mark [unclear].
-5. Return the final output strictly in Markdown format with headings, bullets, and bold labels.
-
-Use this exact structure:
-
-## **Minutes of the Meeting**
-**- Date:** [if present, else "Unknown"]
-**- Attendees:** [if not mentioned, say "Unknown"; if only able to recognise a few, write their names and say "and others"]
-
-### **Summary**
-[2–3 sentences summarizing purpose and tone]
-
-### **Key Agenda and Discussions**
-1. ...
-2. ...
-
-### **Action Items**
-1. ...
-2. ...
-
-### **Open Issues / Concerns**
-1. ...
-2. ...
-
-### **Notes** [minimum 3 sentences]
-- Short factual notes or clarifications.
-
-Be concise, professional, and factually grounded. Maintain Markdown formatting faithfully."""
-
-# ============================================
-# HELPER FUNCTIONS
-# ============================================
-
-def check_file_size(file_bytes: bytes) -> tuple[bool, float]:
-    """
-    Check if uploaded file is within size limit
-    
-    Args:
-        file_bytes: Raw file bytes
-    
-    Returns:
-        tuple: (is_valid, size_in_mb)
-        - is_valid: True if file is under limit
-        - size_in_mb: Actual file size in megabytes
-    """
-    size_mb = len(file_bytes) / (1024 * 1024)  # Convert bytes to MB
-    is_valid = size_mb <= MAX_FILE_SIZE_MB
-    return is_valid, size_mb
-
-# ============================================
-# API ENDPOINTS
-# ============================================
-
-@app.get("/")
-def root():
-    """
-    Health check endpoint
-    
-    Returns API status and version info
-    Used to verify backend is running correctly
-    """
-    return {
-        "message": "🎙️ Meeting Minutes API is running!",
-        "version": "2.0.0",
-        "status": "healthy",
-        "endpoints": {
-            "transcribe": "/transcribe (POST)",
-            "generate_minutes": "/generate-minutes (POST)",
-            "health": "/ (GET)"
-        }
-    }
-
-@app.post("/transcribe", response_model=TranscribeResponse)
-async def transcribe_audio(file: UploadFile = File(...)):
-    """
-    Transcribe audio file to text using Groq Whisper Large v3
-    
-    FLOW:
-    1. Receive audio file from client (Gradio UI)
-    2. Read file bytes into memory
-    3. Validate file size (must be < 25MB)
-    4. Send file to Groq Whisper API
-    5. Receive transcript text
-    6. Validate transcript is not empty
-    7. Return transcript with metadata
-    
-    Args:
-        file: Uploaded audio file
-              Supported formats: mp3, wav, m4a, webm, flac
-    
-    Returns:
-        TranscribeResponse: Contains transcript text and metadata
-    
-    Raises:
-        HTTPException 400: File too large or invalid
-        HTTPException 500: Groq API error
-    """
-    
-    # ========================================
-    # STEP 1: Read uploaded file bytes
-    # ========================================
-    try:
-        file_bytes = await file.read()
-    except Exception as e:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Failed to read uploaded file: {str(e)}"
-        )
-    
-    # ========================================
-    # STEP 2: Validate file size
-    # ========================================
-    is_valid_size, size_mb = check_file_size(file_bytes)
-    
-    if not is_valid_size:
-        raise HTTPException(
-            status_code=400,
-            detail=f"File too large ({size_mb:.2f}MB). Maximum allowed is {MAX_FILE_SIZE_MB}MB. "
-                   f"Please upload a shorter recording or compress the audio."
-        )
-    
-    # ========================================
-    # STEP 3: Call Groq Whisper API
-    # ========================================
-    try:
-        # Create transcription request
-        # Note: file parameter expects tuple of (filename, bytes)
-        transcription = groq_client.audio.transcriptions.create(
-            file=(file.filename, file_bytes),  # Tuple: (name, bytes)
-            model=WHISPER_MODEL,               # whisper-large-v3
-            temperature=WHISPER_TEMPERATURE,    # 0.1 for slightly varied but consistent output
-            response_format="text"             # Returns plain text (not JSON)
-        )
-        
-        # Extract transcript text from response
-        # When response_format="text", the response IS the text string
-        transcript_text = transcription
-        
-    except Exception as e:
-        # Catch any Groq API errors (rate limits, network issues, etc.)
-        raise HTTPException(
-            status_code=500,
-            detail=f"Transcription failed: {str(e)}. Please try again."
-        )
-    
-    # ========================================
-    # STEP 4: Validate transcript is not empty
-    # ========================================
-    if not transcript_text or len(transcript_text.strip()) == 0:
-        raise HTTPException(
-            status_code=400,
-            detail="No speech detected in audio file. Please ensure the recording contains clear speech."
-        )
-    
-    # ========================================
-    # STEP 5: Return successful response
-    # ========================================
-    return TranscribeResponse(
-        transcript=transcript_text.strip(),  # Remove leading/trailing whitespace
-        file_size_mb=round(size_mb, 2),      # Round to 2 decimal places
-        filename=file.filename,               # Original filename
-        success=True                          # Success flag
-    )
-
-@app.post("/generate-minutes", response_model=GenerateMinutesResponse)
-async def generate_minutes(request: GenerateMinutesRequest):
-    """
-    Generate formatted meeting minutes from raw transcript using Groq LLM
-    
-    FLOW:
-    1. Receive raw transcript text
-    2. Validate transcript is not empty
-    3. Build messages array (system prompt + user transcript)
-    4. Call Groq LLM (gpt-oss-120b)
-    5. Receive formatted Markdown minutes
-    6. Validate output is not empty
-    7. Return formatted minutes
-    
-    Args:
-        request: GenerateMinutesRequest containing transcript text
-    
-    Returns:
-        GenerateMinutesResponse: Contains formatted Markdown minutes
-    
-    Raises:
-        HTTPException 400: Empty transcript
-        HTTPException 500: Groq API error
-    """
-    
-    # ========================================
-    # STEP 1: Validate transcript is not empty
-    # ========================================
-    if not request.transcript or len(request.transcript.strip()) == 0:
-        raise HTTPException(
-            status_code=400,
-            detail="Transcript cannot be empty. Please provide a valid transcript."
-        )
-    
-    # ========================================
-    # STEP 2: Build messages array for LLM
-    # ========================================
-    messages = [
-        {
-            "role": "system",
-            "content": MINUTES_SYSTEM_PROMPT
-        },
-        {
-            "role": "user",
-            "content": f"Please convert the following meeting transcript into structured minutes:\n\n{request.transcript}"
-        }
-    ]
-    
-    # ========================================
-    # STEP 3: Call Groq LLM API
-    # ========================================
-    try:
-        # Create chat completion request
-        completion = groq_client.chat.completions.create(
-            model=LLM_MODEL,                      # openai/gpt-oss-120b
-            messages=messages,                     # System prompt + user transcript
-            temperature=LLM_TEMPERATURE,           # 0.1 for deterministic output
-            max_completion_tokens=MAX_COMPLETION_TOKENS,  # 1024 tokens max
-            top_p=1,                              # Standard sampling
-            stream=False,                         # Get complete response at once
-            stop=None                             # No custom stop sequences
-        )
-        
-        # Extract generated minutes from response
-        minutes_text = completion.choices[0].message.content
-        
-    except Exception as e:
-        # Catch any Groq API errors (rate limits, network issues, etc.)
-        raise HTTPException(
-            status_code=500,
-            detail=f"Minutes generation failed: {str(e)}. Please try again."
-        )
-    
-    # ========================================
-    # STEP 4: Validate minutes are not empty
-    # ========================================
-    if not minutes_text or len(minutes_text.strip()) == 0:
-        raise HTTPException(
-            status_code=500,
-            detail="LLM returned empty response. Please try again."
-        )
-    
-    # ========================================
-    # STEP 5: Return successful response
-    # ========================================
-    return GenerateMinutesResponse(
-        minutes=minutes_text.strip(),  # Remove leading/trailing whitespace
-        success=True                    # Success flag
-    )
-
-# ============================================
-# RUN SERVER (for local testing)
-# ============================================
-if __name__ == "__main__":
-    import uvicorn
-    
-    print("🚀 Starting Meeting Minutes Backend...")
-    print("📍 Server will run on: http://localhost:8000")
-    print("📖 API docs available at: http://localhost:8000/docs")
-    print("🔍 Health check: http://localhost:8000")
-    print("\n✅ Available endpoints:")
-    print("   POST /transcribe - Convert audio to text")
-    print("   POST /generate-minutes - Convert transcript to formatted minutes")
-    
-    # Run the FastAPI app with uvicorn
-    uvicorn.run(
-        app,
-        host="0.0.0.0",  # Listen on all network interfaces
-        port=8001,        # Port 8000 (standard for APIs)
-        log_level="info"  # Show request logs
+"""
+Meeting Minutes Generator - Backend API
+Handles audio transcription and minutes generation using Groq
+"""
+
+# ============================================
+# IMPORTS
+# ============================================
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+from groq import Groq
+import os
+from dotenv import load_dotenv
+
+# ============================================
+# LOAD ENVIRONMENT VARIABLES
+# ============================================
+load_dotenv()  # Reads .env file and loads variables
+
+# ============================================
+# INITIALIZE FASTAPI APP
+# ============================================
+app = FastAPI(
+    title="Meeting Minutes API",
+    version="2.0.0",
+    description="Transcribe meeting audio and generate formatted minutes using Groq"
+)
+
+# ============================================
+# INITIALIZE GROQ CLIENT
+# ============================================
+# Get API key from environment
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
+
+# Validate API key exists
+if not GROQ_API_KEY:
+    raise ValueError("❌ GROQ_API_KEY not found in environment. Check your .env file!")
+
+# Create Groq client
+groq_client = Groq(
+    api_key=GROQ_API_KEY,
+    max_retries=2,
+    timeout=120.0
+)
+# ============================================
+# PYDANTIC MODELS (Type Safety & Documentation)
+# ============================================
+
+class TranscribeResponse(BaseModel):
+    """
+    Response model for successful transcription
+    """
+    transcript: str          # The transcribed text
+    file_size_mb: float      # Size of uploaded file in MB
+    filename: str            # Original filename
+    success: bool            # Always True for successful responses
+
+class GenerateMinutesRequest(BaseModel):
+    """
+    Request model for generating minutes
+    """
+    transcript: str          # Raw transcript text from /transcribe
+
+class GenerateMinutesResponse(BaseModel):
+    """
+    Response model for generated minutes
+    """
+    minutes: str            # Formatted Markdown minutes
+    success: bool           # Always True for successful responses
+
+class ErrorResponse(BaseModel):
+    """
+    Response model for errors
+    """
+    error: str              # Error message
+    detail: Optional[str]   # Additional error details
+
+# ============================================
+# CONSTANTS
+# ============================================
+
+# Transcription settings
+MAX_FILE_SIZE_MB = 25              # Groq Whisper limit
+WHISPER_MODEL = "whisper-large-v3"  # Most accurate Whisper model
+WHISPER_TEMPERATURE = 0.1           # Slight randomness for better transcription
+
+# Minutes generation settings
+LLM_MODEL = "openai/gpt-oss-120b"   # Groq LLM model for minutes
+LLM_TEMPERATURE = 0.1               # Low temperature for consistent, factual output
+MAX_COMPLETION_TOKENS = 1024        # Enough for any realistic meeting minutes
+
+# System prompt for minutes generation
+MINUTES_SYSTEM_PROMPT = """You are an assistant that converts meeting transcripts into concise, factual minutes.
+
+Your task:
+1. Remove filler words and disfluencies (uh, um, like, you know).
+2. Restore punctuation and sentence boundaries.
+3. Extract clear, factual minutes.
+4. Do NOT invent facts. If unclear, mark [unclear].
+5. Return the final output strictly in Markdown format with headings, bullets, and bold labels.
+
+Use this exact structure:
+
+## **Minutes of the Meeting**
+**- Date:** [if present, else "Unknown"]
+**- Attendees:** [if not mentioned, say "Unknown"; if only able to recognise a few, write their names and say "and others"]
+
+### **Summary**
+[2–3 sentences summarizing purpose and tone]
+
+### **Key Agenda and Discussions**
+1. ...
+2. ...
+
+### **Action Items**
+1. ...
+2. ...
+
+### **Open Issues / Concerns**
+1. ...
+2. ...
+
+### **Notes** [minimum 3 sentences]
+- Short factual notes or clarifications.
+
+Be concise, professional, and factually grounded. Maintain Markdown formatting faithfully."""
+
+# ============================================
+# HELPER FUNCTIONS
+# ============================================
+
+def check_file_size(file_bytes: bytes) -> tuple[bool, float]:
+    """
+    Check if uploaded file is within size limit
+    
+    Args:
+        file_bytes: Raw file bytes
+    
+    Returns:
+        tuple: (is_valid, size_in_mb)
+        - is_valid: True if file is under limit
+        - size_in_mb: Actual file size in megabytes
+    """
+    size_mb = len(file_bytes) / (1024 * 1024)  # Convert bytes to MB
+    is_valid = size_mb <= MAX_FILE_SIZE_MB
+    return is_valid, size_mb
+
+# ============================================
+# API ENDPOINTS
+# ============================================
+
+@app.get("/")
+def root():
+    """
+    Health check endpoint
+    
+    Returns API status and version info
+    Used to verify backend is running correctly
+    """
+    return {
+        "message": "🎙️ Meeting Minutes API is running!",
+        "version": "2.0.0",
+        "status": "healthy",
+        "endpoints": {
+            "transcribe": "/transcribe (POST)",
+            "generate_minutes": "/generate-minutes (POST)",
+            "health": "/ (GET)"
+        }
+    }
+
+@app.post("/transcribe", response_model=TranscribeResponse)
+async def transcribe_audio(file: UploadFile = File(...)):
+    """
+    Transcribe audio file to text using Groq Whisper Large v3
+    
+    FLOW:
+    1. Receive audio file from client (Gradio UI)
+    2. Read file bytes into memory
+    3. Validate file size (must be < 25MB)
+    4. Send file to Groq Whisper API
+    5. Receive transcript text
+    6. Validate transcript is not empty
+    7. Return transcript with metadata
+    
+    Args:
+        file: Uploaded audio file
+              Supported formats: mp3, wav, m4a, webm, flac
+    
+    Returns:
+        TranscribeResponse: Contains transcript text and metadata
+    
+    Raises:
+        HTTPException 400: File too large or invalid
+        HTTPException 500: Groq API error
+    """
+    
+    # ========================================
+    # STEP 1: Read uploaded file bytes
+    # ========================================
+    try:
+        file_bytes = await file.read()
+    except Exception as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Failed to read uploaded file: {str(e)}"
+        )
+    
+    # ========================================
+    # STEP 2: Validate file size
+    # ========================================
+    is_valid_size, size_mb = check_file_size(file_bytes)
+    
+    if not is_valid_size:
+        raise HTTPException(
+            status_code=400,
+            detail=f"File too large ({size_mb:.2f}MB). Maximum allowed is {MAX_FILE_SIZE_MB}MB. "
+                   f"Please upload a shorter recording or compress the audio."
+        )
+    
+    # ========================================
+    # STEP 3: Call Groq Whisper API
+    # ========================================
+    try:
+        # Create transcription request
+        # Note: file parameter expects tuple of (filename, bytes)
+        transcription = groq_client.audio.transcriptions.create(
+            file=(file.filename, file_bytes),  # Tuple: (name, bytes)
+            model=WHISPER_MODEL,               # whisper-large-v3
+            temperature=WHISPER_TEMPERATURE,    # 0.1 for slightly varied but consistent output
+            response_format="text"             # Returns plain text (not JSON)
+        )
+        
+        # Extract transcript text from response
+        # When response_format="text", the response IS the text string
+        transcript_text = transcription
+        
+    except Exception as e:
+        # Catch any Groq API errors (rate limits, network issues, etc.)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Transcription failed: {str(e)}. Please try again."
+        )
+    
+    # ========================================
+    # STEP 4: Validate transcript is not empty
+    # ========================================
+    if not transcript_text or len(transcript_text.strip()) == 0:
+        raise HTTPException(
+            status_code=400,
+            detail="No speech detected in audio file. Please ensure the recording contains clear speech."
+        )
+    
+    # ========================================
+    # STEP 5: Return successful response
+    # ========================================
+    return TranscribeResponse(
+        transcript=transcript_text.strip(),  # Remove leading/trailing whitespace
+        file_size_mb=round(size_mb, 2),      # Round to 2 decimal places
+        filename=file.filename,               # Original filename
+        success=True                          # Success flag
+    )
+
+@app.post("/generate-minutes", response_model=GenerateMinutesResponse)
+async def generate_minutes(request: GenerateMinutesRequest):
+    """
+    Generate formatted meeting minutes from raw transcript using Groq LLM
+    
+    FLOW:
+    1. Receive raw transcript text
+    2. Validate transcript is not empty
+    3. Build messages array (system prompt + user transcript)
+    4. Call Groq LLM (gpt-oss-120b)
+    5. Receive formatted Markdown minutes
+    6. Validate output is not empty
+    7. Return formatted minutes
+    
+    Args:
+        request: GenerateMinutesRequest containing transcript text
+    
+    Returns:
+        GenerateMinutesResponse: Contains formatted Markdown minutes
+    
+    Raises:
+        HTTPException 400: Empty transcript
+        HTTPException 500: Groq API error
+    """
+    
+    # ========================================
+    # STEP 1: Validate transcript is not empty
+    # ========================================
+    if not request.transcript or len(request.transcript.strip()) == 0:
+        raise HTTPException(
+            status_code=400,
+            detail="Transcript cannot be empty. Please provide a valid transcript."
+        )
+    
+    # ========================================
+    # STEP 2: Build messages array for LLM
+    # ========================================
+    messages = [
+        {
+            "role": "system",
+            "content": MINUTES_SYSTEM_PROMPT
+        },
+        {
+            "role": "user",
+            "content": f"Please convert the following meeting transcript into structured minutes:\n\n{request.transcript}"
+        }
+    ]
+    
+    # ========================================
+    # STEP 3: Call Groq LLM API
+    # ========================================
+    try:
+        # Create chat completion request
+        completion = groq_client.chat.completions.create(
+            model=LLM_MODEL,                      # openai/gpt-oss-120b
+            messages=messages,                     # System prompt + user transcript
+            temperature=LLM_TEMPERATURE,           # 0.1 for deterministic output
+            max_completion_tokens=MAX_COMPLETION_TOKENS,  # 1024 tokens max
+            top_p=1,                              # Standard sampling
+            stream=False,                         # Get complete response at once
+            stop=None                             # No custom stop sequences
+        )
+        
+        # Extract generated minutes from response
+        minutes_text = completion.choices[0].message.content
+        
+    except Exception as e:
+        # Catch any Groq API errors (rate limits, network issues, etc.)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Minutes generation failed: {str(e)}. Please try again."
+        )
+    
+    # ========================================
+    # STEP 4: Validate minutes are not empty
+    # ========================================
+    if not minutes_text or len(minutes_text.strip()) == 0:
+        raise HTTPException(
+            status_code=500,
+            detail="LLM returned empty response. Please try again."
+        )
+    
+    # ========================================
+    # STEP 5: Return successful response
+    # ========================================
+    return GenerateMinutesResponse(
+        minutes=minutes_text.strip(),  # Remove leading/trailing whitespace
+        success=True                    # Success flag
+    )
+
+# ============================================
+# RUN SERVER (for local testing)
+# ============================================
+if __name__ == "__main__":
+    import uvicorn
+    
+    print("🚀 Starting Meeting Minutes Backend...")
+    print("📍 Server will run on: http://localhost:8000")
+    print("📖 API docs available at: http://localhost:8000/docs")
+    print("🔍 Health check: http://localhost:8000")
+    print("\n✅ Available endpoints:")
+    print("   POST /transcribe - Convert audio to text")
+    print("   POST /generate-minutes - Convert transcript to formatted minutes")
+    
+    # Run the FastAPI app with uvicorn
+    uvicorn.run(
+        app,
+        host="0.0.0.0",  # Listen on all network interfaces
+        port=8001,        # Port 8000 (standard for APIs)
+        log_level="info"  # Show request logs
     )