Upload app.py

app.py

@@ -23,117 +23,6 @@ from langchain.prompts import PromptTemplate
 load_dotenv()
 import datetime
 
-
-def get_pdf_text(pdf_docs):
-    text=""
-    for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-
-def get_conversational_chain():
-
-    prompt_template = """   
-    Try always to answer only available in the context and please always give perfect answer from the available context , if i say Summerize then Summerize , and if ask key points then give the key points, if i ask who are you then answer 'i am BHAI(Best High-Quality Artificial Intelligence) Made by hk4crprasad' and always give the perfect and correct output, if i say that tell me more about something in the context then tell accordingly., 
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.5)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-    return chain
-
-
-
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
-    new_db = FAISS.load_local("faiss_index", embeddings)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    
-    pdfans = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    return pdfans["output_text"]
-    
-
-url = "https://y39t47-8080.csb.app/search"
-
-weathe_key = os.getenv("WEATHER_KEY")
-def print_weather_data(api_key, location):
-    base_url = "http://api.weatherstack.com/forecast"
-    params = {"access_key": api_key, "query": location}
-
-    try:
-        response = requests.get(base_url, params=params)
-        data = response.json()
-
-        if response.status_code == 200:
-            # Extract relevant weather information
-            weather_info = data.get("current", {})
-            location_info = data.get("location", {})
-            temperature = weather_info.get("temperature")
-            description = weather_info.get("weather_descriptions", [])[0]
-            humidity = weather_info.get("humidity")
-            localtime = location_info.get("localtime")
-            country = location_info.get("country")
-
-            # Extract forecast information
-            forecast_info = data.get("forecast", {})
-            forecast_str = ""
-            if forecast_info:
-                forecast_date = list(forecast_info.keys())[0]
-                forecast_temp_min = forecast_info[forecast_date].get("mintemp")
-                forecast_temp_max = forecast_info[forecast_date].get("maxtemp")
-                forecast_avg_temp = forecast_info[forecast_date].get("avgtemp")
-
-                forecast_str = f"\nForecast for {forecast_date}: Min Temp {forecast_temp_min}°C, Max Temp {forecast_temp_max}°C, Avg Temp {forecast_avg_temp}°C"
-
-            # Build and return the weather report
-            report = (
-                f"Weather Report for {location}, {country}\n"
-                f"Temperature: {temperature}°C\n"
-                f"Condition: {description}\n"
-                f"Humidity: {humidity}%\n"
-                f"Observed at: {localtime}{forecast_str}"
-            )
-            print(report)
-            return report
-
-        else:
-            return f"Error: {response.status_code}, {data.get('error', {}).get('info', 'Unknown error')}"
-
-    except Exception as e:
-        return f"An error occurred: {e}"
-        
-#Je t'aime plus que les mots,
-#Plus que les sentiments,
-#Plus que la vie elle-même
 history=[
   {
     "parts": [
@@ -567,6 +456,121 @@ history=[
     ],
     "role": "model"
   },
+  
+
+
+
+
+def get_pdf_text(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+    return  text
+
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+
+def get_conversational_chain():
+
+    prompt_template = """   
+    Try always to answer only available in the context and please always give perfect answer from the available context , if i say Summerize then Summerize , and if ask key points then give the key points, if i ask who are you then answer 'i am BHAI(Best High-Quality Artificial Intelligence) Made by hk4crprasad' and always give the perfect and correct output, if i say that tell me more about something in the context then tell accordingly., 
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+
+    model = ChatGoogleGenerativeAI(model="gemini-pro",
+                             temperature=0.5)
+
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+
+
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    
+    new_db = FAISS.load_local("faiss_index", embeddings)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    
+    pdfans = chain(
+        {"input_documents":docs, "question": user_question}
+        , return_only_outputs=True)
+
+    return pdfans["output_text"]
+    
+
+url = "https://y39t47-8080.csb.app/search"
+
+weathe_key = os.getenv("WEATHER_KEY")
+def print_weather_data(api_key, location):
+    base_url = "http://api.weatherstack.com/forecast"
+    params = {"access_key": api_key, "query": location}
+
+    try:
+        response = requests.get(base_url, params=params)
+        data = response.json()
+
+        if response.status_code == 200:
+            # Extract relevant weather information
+            weather_info = data.get("current", {})
+            location_info = data.get("location", {})
+            temperature = weather_info.get("temperature")
+            description = weather_info.get("weather_descriptions", [])[0]
+            humidity = weather_info.get("humidity")
+            localtime = location_info.get("localtime")
+            country = location_info.get("country")
+
+            # Extract forecast information
+            forecast_info = data.get("forecast", {})
+            forecast_str = ""
+            if forecast_info:
+                forecast_date = list(forecast_info.keys())[0]
+                forecast_temp_min = forecast_info[forecast_date].get("mintemp")
+                forecast_temp_max = forecast_info[forecast_date].get("maxtemp")
+                forecast_avg_temp = forecast_info[forecast_date].get("avgtemp")
+
+                forecast_str = f"\nForecast for {forecast_date}: Min Temp {forecast_temp_min}°C, Max Temp {forecast_temp_max}°C, Avg Temp {forecast_avg_temp}°C"
+
+            # Build and return the weather report
+            report = (
+                f"Weather Report for {location}, {country}\n"
+                f"Temperature: {temperature}°C\n"
+                f"Condition: {description}\n"
+                f"Humidity: {humidity}%\n"
+                f"Observed at: {localtime}{forecast_str}"
+            )
+            print(report)
+            return report
+
+        else:
+            return f"Error: {response.status_code}, {data.get('error', {}).get('info', 'Unknown error')}"
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+        
+#Je t'aime plus que les mots,
+#Plus que les sentiments,
+#Plus que la vie elle-même
 ]
 st.set_page_config(
     page_title="BHAI Chat",
@@ -778,7 +782,7 @@ if pdf_mode:
 else:
     pdf_docs = None
     
-if pdf_docs:
+if pdf_mode:
     prompt = st.chat_input("Write your questions according to the pdf")
     if prompt:      
         prmt  = {'role': 'user', 'parts':[prompt]}