Update main.py

main.py

@@ -1,136 +1,131 @@
-from fastapi import FastAPI, UploadFile, File, Form
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-from groq import Groq
-from langchain_community.document_loaders import WebBaseLoader
-
-import os
-import io
-from dotenv import load_dotenv
-from PIL import Image
-import pytesseract
-import whisper
-
-# Load environment variables
-load_dotenv()
-
-# Tesseract path
-tesseract_cmd = os.getenv("TESSERACT_CMD")
-if tesseract_cmd:
-    pytesseract.pytesseract.tesseract_cmd = tesseract_cmd
-
-# FFmpeg path (adjust this path to match where you extracted FFmpeg)
-ffmpeg_path = r"C:\Users\aliab\Downloads\ffmpeg-7.1.1-essentials_build\ffmpeg-7.1.1-essentials_build\bin"
-if os.path.exists(ffmpeg_path):
-    os.environ["PATH"] += os.pathsep + ffmpeg_path
-
-# File reading libraries
-from docx import Document
-import pandas as pd
-import PyPDF2
-
-app = FastAPI()
-
-# Use Groq API key from .env
-client = Groq(api_key=os.getenv("GROQ_API_KEY"))
-
-UPLOAD_DIR = "uploaded_files"
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-
-MAX_FILE_SIZE_MB = 10
-
-# ---------- File Text Extraction ----------
-def extract_text_from_file(file_path):
-    ext = os.path.splitext(file_path)[-1].lower()
-    if ext == ".txt":
-        with open(file_path, "r", encoding="utf-8") as f:
-            return f.read()
-    elif ext == ".docx":
-        doc = Document(file_path)
-        return "\n".join([para.text for para in doc.paragraphs])
-    elif ext == ".csv":
-        df = pd.read_csv(file_path)
-        return df.to_string(index=False)
-    elif ext == ".pdf":
-        with open(file_path, "rb") as f:
-            reader = PyPDF2.PdfReader(f)
-            return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
-    else:
-        return "❌ Unsupported file type."
-
-# ---------- Chat with File ----------
-@app.post("/chat-with-file")
-async def chat_with_file(file: UploadFile = File(...), question: str = Form(...)):
-    try:
-        contents = await file.read()
-
-        if len(contents) > MAX_FILE_SIZE_MB * 1024 * 1024:
-            return JSONResponse(status_code=400, content={"error": "❌ File too large. Max size is 10MB."})
-
-        file_path = os.path.join(UPLOAD_DIR, file.filename)
-        with open(file_path, "wb") as f:
-            f.write(contents)
-
-        file_content = extract_text_from_file(file_path)
-
-        response = client.chat.completions.create(
-            model="llama3-8b-8192",
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant. Use the uploaded file content to answer questions."},
-                {"role": "user", "content": f"{file_content}\n\nQuestion: {question}"}
-            ]
-        )
-
-        return {"answer": response.choices[0].message.content}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-# ---------- Chat with URL ----------
-class URLQuery(BaseModel):
-    url: str
-    question: str
-
-@app.post("/chat-with-url")
-async def chat_with_url(data: URLQuery):
-    try:
-        os.environ["USER_AGENT"] = "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
-        loader = WebBaseLoader(data.url)
-        documents = loader.load()
-        web_content = "\n".join([doc.page_content for doc in documents])
-
-        response = client.chat.completions.create(
-            model="llama3-8b-8192",
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant. Use the website content to answer the user's question."},
-                {"role": "user", "content": f"Website Content:\n{web_content}\n\nNow answer this question:\n{data.question}"}
-            ]
-        )
-        return {"answer": response.choices[0].message.content}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-# ---------- Extract Text from Image ----------
-@app.post("/extract-text-from-image")
-async def extract_text_from_image(file: UploadFile = File(...)):
-    try:
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents))
-        text = pytesseract.image_to_string(image)
-        return {"answer": text.strip() or "⚠️ No text extracted."}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
-
-# ---------- Transcribe Audio ----------
-@app.post("/transcribe-audio")
-async def transcribe_audio(file: UploadFile = File(...)):
-    try:
-        contents = await file.read()
-        audio_path = os.path.join(UPLOAD_DIR, file.filename)
-        with open(audio_path, "wb") as f:
-            f.write(contents)
-
-        model = whisper.load_model("base")
-        result = model.transcribe(audio_path)
-        return {"answer": result["text"] if result.get("text") else "⚠️ No transcript returned."}
-    except Exception as e:
-        return JSONResponse(status_code=500, content={"error": str(e)})
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+from groq import Groq
+from langchain_community.document_loaders import WebBaseLoader
+
+import os
+import io
+from dotenv import load_dotenv
+from PIL import Image
+import pytesseract
+import whisper
+
+# Load environment variables
+load_dotenv()
+
+# Set tesseract and ffmpeg paths for Hugging Face Spaces
+tesseract_cmd = "tesseract"
+pytesseract.pytesseract.tesseract_cmd = tesseract_cmd
+os.environ["PATH"] += os.pathsep + "/usr/bin"
+
+# File reading libraries
+from docx import Document
+import pandas as pd
+import PyPDF2
+
+app = FastAPI()
+
+# Use Groq API key from .env
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+UPLOAD_DIR = "uploaded_files"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+MAX_FILE_SIZE_MB = 10
+
+# ---------- File Text Extraction ----------
+def extract_text_from_file(file_path):
+    ext = os.path.splitext(file_path)[-1].lower()
+    if ext == ".txt":
+        with open(file_path, "r", encoding="utf-8") as f:
+            return f.read()
+    elif ext == ".docx":
+        doc = Document(file_path)
+        return "\n".join([para.text for para in doc.paragraphs])
+    elif ext == ".csv":
+        df = pd.read_csv(file_path)
+        return df.to_string(index=False)
+    elif ext == ".pdf":
+        with open(file_path, "rb") as f:
+            reader = PyPDF2.PdfReader(f)
+            return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
+    else:
+        return "❌ Unsupported file type."
+
+# ---------- Chat with File ----------
+@app.post("/chat-with-file")
+async def chat_with_file(file: UploadFile = File(...), question: str = Form(...)):
+    try:
+        contents = await file.read()
+
+        if len(contents) > MAX_FILE_SIZE_MB * 1024 * 1024:
+            return JSONResponse(status_code=400, content={"error": "❌ File too large. Max size is 10MB."})
+
+        file_path = os.path.join(UPLOAD_DIR, file.filename)
+        with open(file_path, "wb") as f:
+            f.write(contents)
+
+        file_content = extract_text_from_file(file_path)
+
+        response = client.chat.completions.create(
+            model="llama3-8b-8192",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant. Use the uploaded file content to answer questions."},
+                {"role": "user", "content": f"{file_content}\n\nQuestion: {question}"}
+            ]
+        )
+
+        return {"answer": response.choices[0].message.content}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+# ---------- Chat with URL ----------
+class URLQuery(BaseModel):
+    url: str
+    question: str
+
+@app.post("/chat-with-url")
+async def chat_with_url(data: URLQuery):
+    try:
+        os.environ["USER_AGENT"] = "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
+        loader = WebBaseLoader(data.url)
+        documents = loader.load()
+        web_content = "\n".join([doc.page_content for doc in documents])
+
+        response = client.chat.completions.create(
+            model="llama3-8b-8192",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant. Use the website content to answer the user's question."},
+                {"role": "user", "content": f"Website Content:\n{web_content}\n\nNow answer this question:\n{data.question}"}
+            ]
+        )
+        return {"answer": response.choices[0].message.content}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+# ---------- Extract Text from Image ----------
+@app.post("/extract-text-from-image")
+async def extract_text_from_image(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+        text = pytesseract.image_to_string(image)
+        return {"answer": text.strip() or "⚠️ No text extracted."}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+# ---------- Transcribe Audio ----------
+@app.post("/transcribe-audio")
+async def transcribe_audio(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        audio_path = os.path.join(UPLOAD_DIR, file.filename)
+        with open(audio_path, "wb") as f:
+            f.write(contents)
+
+        model = whisper.load_model("base")
+        result = model.transcribe(audio_path)
+        return {"answer": result["text"] if result.get("text") else "⚠️ No transcript returned."}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})