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Hasitha16 commited on Jul 12

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Update main.py

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main.py +248 -248

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@@ -1,249 +1,249 @@
-from fastapi import FastAPI, Request, Header, HTTPException
-from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
-from fastapi.openapi.utils import get_openapi
-from fastapi.openapi.docs import get_swagger_ui_html
-from pydantic import BaseModel
-from transformers import pipeline
-from io import StringIO
-import os, csv, logging
-from openai import OpenAI
-from app.model import summarize_review, smart_summarize  # import both
-from typing import Optional
-app = FastAPI(
-    title="🧠 NeuroPulse AI",
-    description="Multilingual GenAI for smarter feedback — summarization, sentiment, emotion, aspects, Q&A and tags.",
-    version="2025.1.0",
-    openapi_url="/openapi.json",
-    docs_url=None,
-    redoc_url="/redoc"
-)
-@app.get("/docs", include_in_schema=False)
-def custom_swagger_ui():
-    return get_swagger_ui_html(
-        openapi_url=app.openapi_url,
-        title="🧠 Swagger UI - NeuroPulse AI",
-        swagger_favicon_url="https://cdn-icons-png.flaticon.com/512/3794/3794616.png",
-        swagger_js_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui-bundle.js",
-        swagger_css_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui.css",
-    )
-@app.get("/", response_class=HTMLResponse)
-def root():
-    return """
-    <html>
-    <head>
-        <title>NeuroPulse AI</title>
-        <style>
-            body {
-                font-family: 'Segoe UI', sans-serif;
-                background: linear-gradient(135deg, #f0f4ff, #fef3c7);
-                margin: 0;
-                padding: 60px;
-                text-align: center;
-                color: #1f2937;
-            }
-            .container {
-                background: white;
-                padding: 40px;
-                border-radius: 16px;
-                max-width: 800px;
-                margin: auto;
-                box-shadow: 0 10px 30px rgba(0,0,0,0.08);
-                animation: fadeIn 1s ease-in-out;
-            }
-            @keyframes fadeIn {
-                from {opacity: 0; transform: translateY(20px);}
-                to {opacity: 1; transform: translateY(0);}
-            }
-            h1 {
-                font-size: 36px;
-                margin-bottom: 12px;
-                color: #4f46e5;
-            }
-            p {
-                font-size: 18px;
-                margin-bottom: 32px;
-            }
-            .btn {
-                display: inline-block;
-                margin: 8px;
-                padding: 14px 24px;
-                border-radius: 8px;
-                font-weight: 600;
-                color: white;
-                text-decoration: none;
-                background: linear-gradient(90deg, #4f46e5, #6366f1);
-                transition: all 0.3s ease;
-            }
-            .btn:hover {
-                transform: translateY(-2px);
-                box-shadow: 0 4px 12px rgba(0,0,0,0.1);
-            }
-            .btn.red {
-                background: linear-gradient(90deg, #dc2626, #ef4444);
-            }
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <h1>🧠 Welcome to <strong>NeuroPulse AI</strong></h1>
-            <p>Smarter AI feedback analysis — Summarization, Sentiment, Emotion, Aspects, LLM Q&A, and Metadata Tags.</p>
-            <a class="btn" href="/docs">📘 Swagger UI</a>
-            <a class="btn red" href="/redoc">📕 ReDoc</a>
-        </div>
-    </body>
-    </html>
-    """
-# --- Models ---
-class ReviewInput(BaseModel):
-    text: str
-    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
-    industry: str = "Generic"
-    aspects: bool = False
-    follow_up: str = None
-    product_category: str = None
-    device: str = None
-class BulkReviewInput(BaseModel):
-    reviews: list[str]
-    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
-    industry: Optional[list[str]] = None
-    aspects: bool = False
-    product_category: Optional[list[str]] = None
-    device: Optional[list[str]] = None
-class ChatInput(BaseModel):
-    question: str
-    context: str
-class TranslationInput(BaseModel):
-    text: str
-    target_lang: str = "fr"
-# --- Auth & Logging ---
-VALID_API_KEY = "my-secret-key"
-logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
-# --- Load Models Once ---
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
-emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
-sentiment_pipelines = {
-    "distilbert-base-uncased-finetuned-sst-2-english": pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"),
-    "nlptown/bert-base-multilingual-uncased-sentiment": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
-}
-# --- Analyze (Bulk) ---
-@app.post("/bulk/")
-async def bulk(data: BulkReviewInput, x_api_key: str = Header(None)):
-    if x_api_key != VALID_API_KEY:
-        raise HTTPException(status_code=401, detail="Invalid or missing API key")
-    sentiment_pipeline = sentiment_pipelines[data.model]
-    summaries = summarizer(data.reviews, max_length=80, min_length=20, truncation=True)
-    sentiments = sentiment_pipeline(data.reviews)
-    emotions = emotion_model(data.reviews)
-    results = []
-    for i, review in enumerate(data.reviews):
-        label = sentiments[i]["label"]
-        if "star" in label:
-            stars = int(label[0])
-            label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"
-        result = {
-            "review": review,
-            "summary": summaries[i]["summary_text"],
-            "sentiment": label,
-            "emotion": emotions[i][0]["label"],
-            "aspects": [],
-            "product_category": data.product_category[i] if data.product_category else None,
-            "device": data.device[i] if data.device else None,
-            "industry": data.industry[i] if data.industry else None,
-        }
-        results.append(result)
-    return {"results": results}
-@app.post("/analyze/")
-async def analyze(request: Request, data: ReviewInput, x_api_key: str = Header(None), download: str = None):
-    if x_api_key != VALID_API_KEY:
-        raise HTTPException(status_code=401, detail="Invalid or missing API key")
-    sentiment_pipeline = sentiment_pipelines.get(data.model)
-    summary = smart_summarize(data.text) if request.query_params.get("smart") == "1" else summarize_review(data.text)
-    sentiment = sentiment_pipeline(data.text)[0]
-    label = sentiment["label"]
-    if "star" in label:
-        stars = int(label[0])
-        label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"
-    emotion = emotion_model(data.text)[0][0]["label"]
-    aspects_list = []
-    if data.aspects:
-        for asp in ["battery", "price", "camera"]:
-            if asp in data.text.lower():
-                asp_result = sentiment_pipeline(asp + " " + data.text)[0]
-                aspects_list.append({
-                    "aspect": asp,
-                    "sentiment": asp_result["label"],
-                    "score": asp_result["score"]
-                })
-    follow_up_response = chat_llm(data.follow_up, data.text) if data.follow_up else None
-    return {
-        "summary": summary,
-        "sentiment": {"label": label, "score": sentiment["score"]},
-        "emotion": emotion,
-        "aspects": aspects_list,
-        "follow_up": follow_up_response,
-        "product_category": data.product_category,
-        "device": data.device,
-        "industry": data.industry
-    }
-# --- Translate ---
-@app.post("/translate/")
-async def translate(data: TranslationInput):
-    translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{data.target_lang}")
-    return {"translated_text": translator(data.text)[0]["translation_text"]}
-# --- LLM Agent Chat ---
-@app.post("/chat/")
-async def chat(input: ChatInput, x_api_key: str = Header(None)):
-    if x_api_key != VALID_API_KEY:
-        raise HTTPException(status_code=401, detail="Invalid or missing API key")
-    return {"response": chat_llm(input.question, input.context)}
-def chat_llm(question, context):
-    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-    res = client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are a helpful AI review analyst."},
-            {"role": "user", "content": f"Context: {context}\nQuestion: {question}"}
-        ]
-    )
-    return res.choices[0].message.content.strip()
-# --- Custom OpenAPI ---
-def custom_openapi():
-    if app.openapi_schema:
-        return app.openapi_schema
-    openapi_schema = get_openapi(
-        title=app.title,
-        version=app.version,
-        description="""
-<b><span style='color:#4f46e5'>NeuroPulse AI</span></b> · Smart GenAI Feedback Engine<br>
-Summarize reviews, detect sentiment/emotion, extract aspects, tag metadata, and ask GPT follow-ups.
-""",
-        routes=app.routes
-    )
-    openapi_schema["openapi"] = "3.0.0"
-    app.openapi_schema = openapi_schema
-    return app.openapi_schema
 app.openapi = custom_openapi

+from fastapi import FastAPI, Request, Header, HTTPException
+from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
+from fastapi.openapi.utils import get_openapi
+from fastapi.openapi.docs import get_swagger_ui_html
+from pydantic import BaseModel
+from transformers import pipeline
+from io import StringIO
+import os, csv, logging
+from openai import OpenAI
+from model import summarize_review, smart_summarize
+from typing import Optional
+app = FastAPI(
+    title="🧠 NeuroPulse AI",
+    description="Multilingual GenAI for smarter feedback — summarization, sentiment, emotion, aspects, Q&A and tags.",
+    version="2025.1.0",
+    openapi_url="/openapi.json",
+    docs_url=None,
+    redoc_url="/redoc"
+)
+@app.get("/docs", include_in_schema=False)
+def custom_swagger_ui():
+    return get_swagger_ui_html(
+        openapi_url=app.openapi_url,
+        title="🧠 Swagger UI - NeuroPulse AI",
+        swagger_favicon_url="https://cdn-icons-png.flaticon.com/512/3794/3794616.png",
+        swagger_js_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui-bundle.js",
+        swagger_css_url="https://cdn.jsdelivr.net/npm/[email protected]/swagger-ui.css",
+    )
+@app.get("/", response_class=HTMLResponse)
+def root():
+    return """
+    <html>
+    <head>
+        <title>NeuroPulse AI</title>
+        <style>
+            body {
+                font-family: 'Segoe UI', sans-serif;
+                background: linear-gradient(135deg, #f0f4ff, #fef3c7);
+                margin: 0;
+                padding: 60px;
+                text-align: center;
+                color: #1f2937;
+            }
+            .container {
+                background: white;
+                padding: 40px;
+                border-radius: 16px;
+                max-width: 800px;
+                margin: auto;
+                box-shadow: 0 10px 30px rgba(0,0,0,0.08);
+                animation: fadeIn 1s ease-in-out;
+            }
+            @keyframes fadeIn {
+                from {opacity: 0; transform: translateY(20px);}
+                to {opacity: 1; transform: translateY(0);}
+            }
+            h1 {
+                font-size: 36px;
+                margin-bottom: 12px;
+                color: #4f46e5;
+            }
+            p {
+                font-size: 18px;
+                margin-bottom: 32px;
+            }
+            .btn {
+                display: inline-block;
+                margin: 8px;
+                padding: 14px 24px;
+                border-radius: 8px;
+                font-weight: 600;
+                color: white;
+                text-decoration: none;
+                background: linear-gradient(90deg, #4f46e5, #6366f1);
+                transition: all 0.3s ease;
+            }
+            .btn:hover {
+                transform: translateY(-2px);
+                box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+            }
+            .btn.red {
+                background: linear-gradient(90deg, #dc2626, #ef4444);
+            }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🧠 Welcome to <strong>NeuroPulse AI</strong></h1>
+            <p>Smarter AI feedback analysis — Summarization, Sentiment, Emotion, Aspects, LLM Q&A, and Metadata Tags.</p>
+            <a class="btn" href="/docs">📘 Swagger UI</a>
+            <a class="btn red" href="/redoc">📕 ReDoc</a>
+        </div>
+    </body>
+    </html>
+    """
+# --- Models ---
+class ReviewInput(BaseModel):
+    text: str
+    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
+    industry: str = "Generic"
+    aspects: bool = False
+    follow_up: str = None
+    product_category: str = None
+    device: str = None
+class BulkReviewInput(BaseModel):
+    reviews: list[str]
+    model: str = "distilbert-base-uncased-finetuned-sst-2-english"
+    industry: Optional[list[str]] = None
+    aspects: bool = False
+    product_category: Optional[list[str]] = None
+    device: Optional[list[str]] = None
+class ChatInput(BaseModel):
+    question: str
+    context: str
+class TranslationInput(BaseModel):
+    text: str
+    target_lang: str = "fr"
+# --- Auth & Logging ---
+VALID_API_KEY = "my-secret-key"
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+# --- Load Models Once ---
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
+sentiment_pipelines = {
+    "distilbert-base-uncased-finetuned-sst-2-english": pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english"),
+    "nlptown/bert-base-multilingual-uncased-sentiment": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+}
+# --- Analyze (Bulk) ---
+@app.post("/bulk/")
+async def bulk(data: BulkReviewInput, x_api_key: str = Header(None)):
+    if x_api_key != VALID_API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid or missing API key")
+    sentiment_pipeline = sentiment_pipelines[data.model]
+    summaries = summarizer(data.reviews, max_length=80, min_length=20, truncation=True)
+    sentiments = sentiment_pipeline(data.reviews)
+    emotions = emotion_model(data.reviews)
+    results = []
+    for i, review in enumerate(data.reviews):
+        label = sentiments[i]["label"]
+        if "star" in label:
+            stars = int(label[0])
+            label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"
+        result = {
+            "review": review,
+            "summary": summaries[i]["summary_text"],
+            "sentiment": label,
+            "emotion": emotions[i][0]["label"],
+            "aspects": [],
+            "product_category": data.product_category[i] if data.product_category else None,
+            "device": data.device[i] if data.device else None,
+            "industry": data.industry[i] if data.industry else None,
+        }
+        results.append(result)
+    return {"results": results}
+@app.post("/analyze/")
+async def analyze(request: Request, data: ReviewInput, x_api_key: str = Header(None), download: str = None):
+    if x_api_key != VALID_API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid or missing API key")
+    sentiment_pipeline = sentiment_pipelines.get(data.model)
+    summary = smart_summarize(data.text) if request.query_params.get("smart") == "1" else summarize_review(data.text)
+    sentiment = sentiment_pipeline(data.text)[0]
+    label = sentiment["label"]
+    if "star" in label:
+        stars = int(label[0])
+        label = "NEGATIVE" if stars <= 2 else "NEUTRAL" if stars == 3 else "POSITIVE"
+    emotion = emotion_model(data.text)[0][0]["label"]
+    aspects_list = []
+    if data.aspects:
+        for asp in ["battery", "price", "camera"]:
+            if asp in data.text.lower():
+                asp_result = sentiment_pipeline(asp + " " + data.text)[0]
+                aspects_list.append({
+                    "aspect": asp,
+                    "sentiment": asp_result["label"],
+                    "score": asp_result["score"]
+                })
+    follow_up_response = chat_llm(data.follow_up, data.text) if data.follow_up else None
+    return {
+        "summary": summary,
+        "sentiment": {"label": label, "score": sentiment["score"]},
+        "emotion": emotion,
+        "aspects": aspects_list,
+        "follow_up": follow_up_response,
+        "product_category": data.product_category,
+        "device": data.device,
+        "industry": data.industry
+    }
+# --- Translate ---
+@app.post("/translate/")
+async def translate(data: TranslationInput):
+    translator = pipeline("translation", model=f"Helsinki-NLP/opus-mt-en-{data.target_lang}")
+    return {"translated_text": translator(data.text)[0]["translation_text"]}
+# --- LLM Agent Chat ---
+@app.post("/chat/")
+async def chat(input: ChatInput, x_api_key: str = Header(None)):
+    if x_api_key != VALID_API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid or missing API key")
+    return {"response": chat_llm(input.question, input.context)}
+def chat_llm(question, context):
+    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+    res = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful AI review analyst."},
+            {"role": "user", "content": f"Context: {context}\nQuestion: {question}"}
+        ]
+    )
+    return res.choices[0].message.content.strip()
+# --- Custom OpenAPI ---
+def custom_openapi():
+    if app.openapi_schema:
+        return app.openapi_schema
+    openapi_schema = get_openapi(
+        title=app.title,
+        version=app.version,
+        description="""
+<b><span style='color:#4f46e5'>NeuroPulse AI</span></b> · Smart GenAI Feedback Engine<br>
+Summarize reviews, detect sentiment/emotion, extract aspects, tag metadata, and ask GPT follow-ups.
+""",
+        routes=app.routes
+    )
+    openapi_schema["openapi"] = "3.0.0"
+    app.openapi_schema = openapi_schema
+    return app.openapi_schema
 app.openapi = custom_openapi